






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase        =							logging.get_logger(__name__)

__lowerCAmelCase        =							{
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class   __SCREAMING_SNAKE_CASE       (__A							,       __A       ):


  """simple docstring"""


  _a  : Tuple      			=     '''nat'''

  _a  : List[str]      			=     {
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
  }







  def __init__(			self   ,	UpperCamelCase__=4   ,	UpperCamelCase__=3   ,	UpperCamelCase__=64   ,	UpperCamelCase__=[3, 4, 6, 5]   ,	UpperCamelCase__=[2, 4, 8, 16]   ,	UpperCamelCase__=7   ,	UpperCamelCase__=3.0   ,	UpperCamelCase__=True   ,	UpperCamelCase__=0.0   ,	UpperCamelCase__=0.0   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=1e-5   ,	UpperCamelCase__=0.0   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(**UpperCamelCase__	)

         a_						     =			patch_size
         a_						     =			num_channels
         a_						     =			embed_dim
         a_						     =			depths
         a_						     =			len(UpperCamelCase__	)
         a_						     =			num_heads
         a_						     =			kernel_size
         a_						     =			mlp_ratio
         a_						     =			qkv_bias
         a_						     =			hidden_dropout_prob
         a_						     =			attention_probs_dropout_prob
         a_						     =			drop_path_rate
         a_						     =			hidden_act
         a_						     =			layer_norm_eps
         a_						     =			initializer_range
         # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         a_						     =			int(embed_dim * 2 ** (len(UpperCamelCase__	) - 1)	)
         a_						     =			layer_scale_init_value
         a_						     =			['stem'] + [f'stage{idx}' for idx in range(1   ,	len(UpperCamelCase__	) + 1	)]
         a_     ,	a_						     =			get_aligned_output_features_output_indices(
             out_features=UpperCamelCase__   ,	out_indices=UpperCamelCase__   ,	stage_names=self.stage_names	)












'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__lowerCAmelCase        =							logging.get_logger(__name__)


__lowerCAmelCase        =							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCAmelCase        =							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowerCAmelCase        =							{"facebook/blenderbot-3B": 1_28}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			(
           list(range(ord('!'   )   ,       ord('~'   ) + 1   )   ) + list(range(ord('¡'   )   ,       ord('¬'   ) + 1   )   ) + list(range(ord('®'   )   ,       ord('ÿ'   ) + 1   )   )
       )
       a_						     =			bs[:]
       a_						     =			0
       for b in range(2**8   ):
              if b not in bs:
                     bs.append(lowercase_   )
                     cs.append(2**8 + n   )
                     n += 1
       a_						     =			[chr(lowercase_   ) for n in cs]
       return dict(zip(lowercase_   ,       lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			set()
       a_						     =			word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)   )
              a_						     =			char
       return pairs




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     VOCAB_FILES_NAMES
  _a  : Union[str, Any]      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : Optional[int]      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : str      			=     ['''input_ids''', '''attention_mask''']







  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__="replace"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="<mask>"   ,	UpperCamelCase__=False   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else bos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else eos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else sep_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else cls_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else unk_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else mask_token

         super().__init__(
             errors=UpperCamelCase__   ,	bos_token=UpperCamelCase__   ,	eos_token=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	add_prefix_space=UpperCamelCase__   ,	**UpperCamelCase__   ,	)

         with open(UpperCamelCase__   ,	encoding='utf-8'	) as vocab_handle:
                a_						     =			json.load(UpperCamelCase__	)
         a_						     =			{v: k for k, v in self.encoder.items()}
         a_						     =			errors  # how to handle errors in decoding
         a_						     =			bytes_to_unicode()
         a_						     =			{v: k for k, v in self.byte_encoder.items()}
         with open(UpperCamelCase__   ,	encoding='utf-8'	) as merges_handle:
                a_						     =			merges_handle.read().split('\n'	)[1:-1]
         a_						     =			[tuple(merge.split()	) for merge in bpe_merges]
         a_						     =			dict(zip(UpperCamelCase__   ,	range(len(UpperCamelCase__	)	)	)	)
         a_						     =			{}
         a_						     =			add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         a_						     =			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def    _a       (			self	):






         """simple docstring"""


         return len(self.encoder	)







  def    _a       (			self	):






         """simple docstring"""


         return dict(self.encoder   ,	**self.added_tokens_encoder	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if token in self.cache:
                return self.cache[token]
         a_						     =			tuple(UpperCamelCase__	)
         a_						     =			get_pairs(UpperCamelCase__	)

         if not pairs:
                return token

         while True:
                a_						     =			min(UpperCamelCase__   ,	key=lambda UpperCamelCase__	: self.bpe_ranks.get(UpperCamelCase__   ,	float('inf'	)	)	)
                if bigram not in self.bpe_ranks:
                       break
                a_     ,	a_						     =			bigram
                a_						     =			[]
                a_						     =			0
                while i < len(UpperCamelCase__	):
                       try:
                              a_						     =			word.index(UpperCamelCase__   ,	UpperCamelCase__	)
                       except ValueError:
                              new_word.extend(word[i:]	)
                              break
                       else:
                              new_word.extend(word[i:j]	)
                              a_						     =			j

                       if word[i] == first and i < len(UpperCamelCase__	) - 1 and word[i + 1] == second:
                              new_word.append(first + second	)
                              i += 2
                       else:
                              new_word.append(word[i]	)
                              i += 1
                a_						     =			tuple(UpperCamelCase__	)
                a_						     =			new_word
                if len(UpperCamelCase__	) == 1:
                       break
                else:
                       a_						     =			get_pairs(UpperCamelCase__	)
         a_						     =			' '.join(UpperCamelCase__	)
         a_						     =			word
         return word







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for token in re.findall(self.pat   ,	UpperCamelCase__	):
                a_						     =			''.join(
                    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__	).split(' '	)	)
         return bpe_tokens







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.encoder.get(UpperCamelCase__   ,	self.encoder.get(self.unk_token	)	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.decoder.get(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			''.join(UpperCamelCase__	)
         a_						     =			bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'   ,	errors=self.errors	)
         return text







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__	):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                return
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as f:
                f.write(json.dumps(self.encoder   ,	indent=2   ,	sort_keys=UpperCamelCase__   ,	ensure_ascii=UpperCamelCase__	) + '\n'	)

         a_						     =			0
         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as writer:
                writer.write('#version: 0.2\n'	)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,	key=lambda UpperCamelCase__	: kv[1]	):
                       if index != token_index:
                              logger.warning(
                                  f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                  ' Please check that the tokenizer is not corrupted!'	)
                              a_						     =			token_index
                       writer.write(' '.join(UpperCamelCase__	) + '\n'	)
                       index += 1

         return vocab_file, merge_file







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False	):






         """simple docstring"""


         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=UpperCamelCase__   ,	token_ids_a=UpperCamelCase__   ,	already_has_special_tokens=UpperCamelCase__	)

         if token_ids_a is None:
                return [1] + ([0] * len(UpperCamelCase__	)) + [1]
         return [1] + ([0] * len(UpperCamelCase__	)) + [1, 1] + ([0] * len(UpperCamelCase__	)) + [1]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=False   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			kwargs.pop('add_prefix_space'   ,	self.add_prefix_space	)
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__	) > 0 and not text[0].isspace()):
                a_						     =			' ' + text
         return (text, kwargs)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         return token_ids_a + [self.eos_token_id]







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for is_user, text in conversation.iter_texts():
                if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(' ' + text	)
                else:
                       # Generated responses should contain them already.
                       inputs.append(UpperCamelCase__	)

         a_						     =			'  '.join(UpperCamelCase__	)
         a_						     =			self.encode(UpperCamelCase__	)
         if len(UpperCamelCase__	) > self.model_max_length:
                a_						     =			input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'	)
         return input_ids




